High-performance solutions of geographically weighted regression in R

Lu, Binbin and Hu, Yigong and Murakami, Daisuke and Brunsdon, Chris and Comber, Alexis and Charlton, Martin and Harris, Paul (2022) High-performance solutions of geographically weighted regression in R. Geo-spatial Information Science, 25 (4). pp. 536-549. ISSN 1009-5020

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Abstract

As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

Item Type: Article
Subjects: Impact Archive > Geological Science
Depositing User: Managing Editor
Date Deposited: 06 Jun 2023 05:50
Last Modified: 30 Nov 2023 04:02
URI: http://research.sdpublishers.net/id/eprint/2430

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